Saturday, March 29, 2008

Conformed Dimension

The word of Enterprise Data Warehouse is very nice at the first glance, yet when businesses want to estimate its cost, they usually prefer to have some important data marts that drive their most important business goals; in order to break its enormous cost. Beside, having these kinds of data marts could be a huge problem especially when those could not integrated with each other during the time that those are created.
Having bus architecture is undoubtedly one of the vital parts of building an enterprise data warehouse during the time, for it will provide the join possibility between the data marts by specifying the conformed dimensions.
Conformed dimensions are what which are common between data marts and make the drill across possible. Off course, it could not be eliminated from enterprise main features.
Finally, the most common problem that you may encounter during building the conformed dimensions is different level of grains that different data marts are needed, so it is recommended that you design dimension’s levels correctly which will even be helpful for drilling through actions.

Saturday, March 1, 2008

Factless Fact Tables

I have recently got familiar with the concept of “Factless Fact Tables”, and due to the fact that it was very interesting for me, I like to share it with you, if you are not already familiar with it.
The “Factless Fact Table” is a table which is similar to Fact Table except for having any measure; I mean that this table just has the links to the dimensions. But what is the benefit?
These tables enable you to track events; indeed they are for recording events.
Another kind of this Table is known as “Factless Coverage Table” which is very usefull. Imagine that you have a retail stores and each store has its own promotion policy. It would be very complicated if you wanted to answer this sort of question: “Which products were on promotion that didn't sell?”
The best way for covering these kinds of questions is the coverage tables. These tables hold the coverage data for answering these questions.
I also recommend reading “Factless Fact Tables” that was written by Ralph Kimball.